About Random forest algorithm

1. RANDOM FOREST
ALGORITHM
RASHID ANSARI
170847980002
MTECH (ACDS)

2. INTRODUCTION
Random forest algorithm is a supervised classification
algorithm.
More trees in the forest the more robust the forest
looks like. Same way in random forest classifier, the
higher the number of trees in the forest gives the
high accuracy results.

3. INTRODUCTION
IN SIMPLE WORDS
Random forest builds multiple decision trees
And mergers them together to get a more accurate
And stable prediction

4. EXAMPLE
SOURCE-

5. How Random Forest Algorithm Works
Data
Data
Subset 1
Data
Subset 2
Data
Subset 3
Data
Subset n

6. How Random Forest Algorithm Works

7. Random Forest pseudocode
Randomly select “k” features from total “m” features.
K<m
Among “k” features, calculate the node “d” using the
best split point.
Split the node into daughter modes using the best
split.
Repeat 1 to 3 steps until “I” number of nodes has
been reached.
Build forest by repeating steps 1 to 4 for “n” number
times to create “n” number of trees.

8. APPLICATION

9. ADVANTAGES
 The overfitting problem will never come when we use the
random forest algorithm in any classification problem.
 It can be used for both classification and regression task.

10. THANKYOU